{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from pandas import Series, DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 直方图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "s = Series(np.random.randn(1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([   4.,   22.,   65.,  148.,  228.,  245.,  160.,   84.,   37.,    7.]),\n",
       " array([-3.30986998, -2.67775932, -2.04564866, -1.413538  , -0.78142734,\n",
       "        -0.14931668,  0.48279398,  1.11490464,  1.74701531,  2.37912597,\n",
       "         3.01123663]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.hist(s, rwidth=0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MC/+GAT7SPZW/4iX5eeAAC3+H3gQcrKo/m0hfaznoJWktWNOXbiRpLTDoJalxBr0kNc6g\nl6TGGfSS1DiDXpIaZ9BLUuMMeklq3P8ASaG498sTMYEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116e0da58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a = np.arange(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSqcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116f984e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(a,rwidth=0.9)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "re = plt.hist(s, rwidth=0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(re)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   4.,   22.,   65.,  148.,  228.,  245.,  160.,   84.,   37.,    7.])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "re[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-3.30986998, -2.67775932, -2.04564866, -1.413538  , -0.78142734,\n",
       "       -0.14931668,  0.48279398,  1.11490464,  1.74701531,  2.37912597,\n",
       "        3.01123663])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "re[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<a list of 10 Patch objects>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "re[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MC/+GAT7SPZW/4iX5eeAAC3+H3gQcrKo/m0hfaznoJWktWNOXbiRpLTDoJalxBr0kNc6g\nl6TGGfSS1DiDXpIaZ9BLUuMMeklq3P8ASaG498sTMYEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116fa0eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nASeerKITZPJIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1174d7400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(s, rwidth=0.9,bins=20, color='r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 密度图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x117bf20b8>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.plot(kind='kde')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jIvUiknWhvk5lNbHttSNn6ez12nBUGE1ITuTGJVN5quokXX1et+OYKOHo/StV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\n/C+ISguN/ghukAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117d42dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
